Developing A Framework for Management Decision Making for the Tasmanian Sand Flathead Fishery
Tom Carruthers tom@bluematterscience.com
08 May, 2024
Disclaimer
The following work is preliminary and intended only as tool for eliciting feedback on data, modelling and other aspects of these fisheries.
None of these results are final.
These analyses do not necessarily reflect the point of view of IMAS or other funders and in no way anticipate future policy in this area.
Objective
Develop an MSE framework for the Tasmanian Sand Flathead fishery that can inform management decision making including research prioritization, assessment methodology, specification of fishing regulations and enforcement.
Project details
‘Development of a draft operating model in openMSE for the assessment and management strategy evaluation of Southern Sand Flathead in Tasmania.’
| Term | 15/03/2024 - 1/7/2024 |
| Funding body | University of Tasmania |
| Funding stream | Sub contract |
| Project No. | T0030292 |
| Project Partners | IMAS, Blue Matter Science Ltd. |
| Blue Matter Team | Drs. Tom Carruthers & Adrian Hordyk |
| IMAS Principal Investigators | Dr. Sean Tracey |
Current Issues
Tabulated below are a list of current issues / assumptions that should be addressed at the current stage of framework development.
| Issue | Notes |
|---|---|
| Length at 50% maturity, and logistic slope | From an online report by NRE (‘around 27 cm’) |
| Selectivity of largest / oldest fish | Currently assumed to be flat-topped, asymptotic. |
| Natural mortality rate | Using value of 0.28 from Kruek et al. 2023 but what is the origin of this? |
| Minimum size limit of 32cm | Is this correct, for all areas? |
| Background rate of discarding | Assumed to be zero - but is this accurate? We can do bag limit modelling but need CPUE vs release rate by trip (baglimit) |
| Observation error a placeholder to get demo MPs working | Later these observation processes can be characterized statistically |
| Observation biases assumed to be nil | For now, observed catches, indices etc are assumed to be unbiased and not hyperstable or hyperdeplete |
| Implementation assumed to be perfect | For now, for demonstration purposes, any management advice is assumed to be followed exactly |
| Nominal Effort could be improved as an index of exploitation rate | Can we derive effort / habitat area. There is the potential to borrow information on catchability among areas/models - priors, metaanalysis, EM. |
| Catches are expanded to totals using expansion factor - no uncertainty | How can we get observation error in total catches? How are expansion factors calculated - can we do bootstrapping etc? |
| Discard mortality rate assumed to be 9% but from a study elsehwere | Lyle et al. 2006. This is used to include discard mortality in total catch data (in model conditioning [Catch = ExpFac x (Kept + Rel * DiscMort)] and used in projections that would affect any kind of regulation affecting discarding such as size limits, bag limits etc. |
| Total recreational effort | currently calculated by Duration_hrs x Npersons x ExpWt (what is the ‘expansion factor’??) |
Study Area
Figure 1. Study area. Area definitions (top left), areas of high recreational effort (top middle), areas of research focus (top right), commercial effort distribution (bottom left), commercial catch per unit effort (bottom right).
Operating models
Stochasticity in life history characteristics
In order to include plausible uncertainty in the life-history dynamics for sand flathead, frequentist models of somatic growth and the length-weight relationship were fitted to data and parameter values draw from the variance covariance matrix arising from those fits (Figures 2 and 3)
Figure 2. Generation of stochastic life history parameters for a preliminary operating model for Flinders Island. Top left is the fit of a preliminary von Bertalanffy somatic growth model to observed age-length data. Top right is the correlation among simulated asympotic length (Linf) and maximum growth rate (K) parameters drawn from the variance-covariance matrix of the model fit. Bottom left is the simualted natual mortality rate (M) given a fixed ratio of M/K and a lognormal error with CV of 10%. Bottom right is the simulated length at 50% maturity (L50) given a fixed ratio of L50/Linf and a lognormal error with CV of 10%.
Figure 3. Generation of stochastic weight-length parameters for a preliminary operating model for Flinders Island. Top left is the fit of a preliminary weight length (W=aL^b) growth model to observed length-weight data. Top right is the correlation among the slope (a) and power (b) parameters drawn from the variance-covariance matrix of the model fit.
Software and Code
IMAS flathead GitHub repository (private) - requests for access go to Sean Tracey
openMSE (MSEtool, DLMtool, SAMtool R libraries)
Rapid Conditioning Model (RCM) (Huynh 2023)
Recent Presentations
Krueck, N. 2023. Commercial logbook data for fishery assessments of Southern Sand Flathead
Krueck, N. 2023. State-wide Rec Fishing Surveys TAS
Reports
Coulson, P, et al. 2022. Fishery-independent monitoring of sand flathead popuation dynamics
Krueck et al. 2023 Stock Assesssment
Research papers
Acknowledgements
Sean Tracey, Nils Krueck, Kate Stark, Alyssa Marshall, Peter Coulson, Barrett Wolfe, Katie Cresswell, Ruth Sharples.
Appendix 1: MSE terminology
Operating models
An operating model is a theoretical description of fishery and population dynamics used for the testing of management strategies that could include, for example, data collection protocols, stock assessment methods, harvest control rules, enforcement policies and reference points. In fisheries, operating models are used in closed-loop simulation to test management procedures (aka. harvest strategy) accounting for feedbacks between the system, data, management procedure and implementation. A management procedure is any codifable rule that calculates management advice from data. Management Strategy Evaluation uses closed-loop simulation of management procedures as a core technical component but is a wider process of stakeholder and manager engagement that identifies system uncertainties, performance metrics, viable management procedures, ultimately aiming to adopt an MP for the provision of management advice for an established time period.
Reference Case Operating Models
The reference case operating model is used as the single ‘base’ operating model from which reference set and robustness set operating models are specified. Reference and robustness tests are typically 1-factor departures from the reference case OM, however sometimes reference set OMs are organized in a factorial grid across primary axes of uncertainty.
Reference Set Operating Models
Reference set operating models span a plausible range of the core uncertainties for states of nature. These are often the types of alternative parameterizations or assumptions that would be included in a stock assessment sensitivity analysis.
The role of the reference set operating models is to provide the central basis for evaluating the performance of candidate management procedures, for example rejecting badly performing harvest strategies.
Robustness Set Operating Models
Robustness set operating models are intended to include additional sources of uncertainty for providing further discrimination among management procedures that perform comparably among reference set operating models.
Robustness operating models often represent system states of nature that are not empirically informed or are hypotheses of a subset of stakeholders.